Abstract
Water is essential for the survival of plants, animals, and human beings. It is imperative to effectively manage and protect aquatic resources to sustain life on Earth. Small tributaries are an important water resource originating in mountain areas, they play an important role in river network evolution and water transmission and distribution. Snow and cloud cover cast shadows leading to misclassification in optical remote sensing images, especially in high-mountain regions. In this study, we effectively extract small and open-surface river information in the Upper Yellow River by fusing Sentinel-2 with 10 m resolution optical imagery corresponding to average discharge of the summer flood season and the 90 m digital elevation model (DEM) data. To effectively minimize the impact of the underlying surface, the study area was divided into five sub-regions according to underlying surface, terrain, and altitude features. We minimize the effects of cloud, snow, and shadow cover on the extracted river surface via a modified normalized difference water index (MNDWI), revised normalized difference water index (RNDWI), automated water extraction index (AWEI), and Otsu threshold method. Water index calculations and water element extractions are operated on the Google Earth Engine (GEE) platform. The river network vectors derived from the DEM data are used as constraints to minimize background noise in the extraction results. The accuracy of extracted river widths is assessed using different statistical indicators such as the R-square (R2) value, root mean square error (RMSE), mean bias error (MBE). The results show the integrity of the extracted small river surface by the RNDWI index is optimal. Overall, the statistical evaluation indicates the accuracy of the extracted river widths is satisfactory. The effective river width that can be accurately extracted based on satellite images is three times the image resolution. Sentinel-2 MSI images with a spatial resolution of 10 m are used to find that the rivers over 30 m wide can be connectedly, accurately extracted with the proposed method. Results of this work can enrich the river width database in the northeast Tibetan Plateau and its boundary region. The river width information may provide a foundation for studying the spatiotemporal changes in channel geometry of river systems in high-mountain regions. They can also supplement the necessary characteristic river widths information for the river network in unmanned mountain areas, which is of great significance for the accurate simulation of the runoff process in the hydrological model.
Highlights
River systems across the globe develop from source regions, stretch over the land, and flow into the sea as the “blood vessels of the earth”
We evaluated the accuracy of the river surface extraction results according to (1) the qualitative riverWineteegvraalluitayteadndthloecaactciounraaccycuorfatchyearnivder(2s)utrhfeacqeueaxnttriatacttiivoenrrievseurlwtsiadctcho. rTdhinegqutoal(i1ta) ttihvee qeuvaalluitaattiiovne wrivaesrpinetrefogrrmalietdy abnydsluopcaetripoonsaitcicounraacnyaalynsdis(2w) tihthe qGuRaWntLitaftriovme riAvlelrenwiadntdh.PTahveeqlsukaylit[a1t0iv].e eTvhaelulaintieoanr wreagsrepsseirofnoramcceudrabcyy osuf prievreprowsiitdiothnsawnaaslyesvisalwuaittehdGuRsiWngLinfrsoimtu rAivlleernwainddthsPafrvoemlskthye[3190]h.yTdhreololigniecaarl rsetagtrieosnssioinn adcactueroafciymoafgreivs.erBwasieddthosnwthase elivtearluatautered[u5s1i–n5g7]in, tshietulirnievaerr rwegidrethsssifornommothdeel3w9 hays devroallougaitceadl sutsaitniognsstaitnisdtiactael oinf dimicaagtoerss
By comparing the extraction results of the connected rivers (Figure 7, width > 30 m rivers) based on Sentinel-2 images with global river widths from Landsat (GRWL) centerline products from Allen and Pavelsky [10], and comparing the extraction results of the connected rivers with river networks from the SRTM 90 m digital elevation model (DEM) data (Figure 8, ≥Order 4 rivers), we evaluated the spatial distribution of rivers and the detailed degree of river extraction in this area
Summary
River systems across the globe develop from source regions, stretch over the land, and flow into the sea as the “blood vessels of the earth”. Water bodies interact with the atmosphere, vegetation, environment, and geomorphology to play an important role in regional economic and environmental sustainability, drinking water safety, and ecological security [1,2,3,4,5] Spatial characteristics such as river width, water surface area, river channel form, and braiding index are essential for discharge estimation [6,7], flood forecasting [8,9], climate change research [10,11,12], hydro-geomorphological process assessment [13,14], and landscape evolution analysis [15]. The GEE cloud platform integrates many open source remote sensing images and various derivative products, providing strong support for efficient water body extraction
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